Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challen...Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challenges for its extensive incorporation into power grids.Thus,enhancing the precision of PV power prediction is particularly important.Although existing studies have made progress in short-term prediction,issues persist,particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data.These factors hinder improvements in PV power prediction performance.To overcome these challenges,this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning.First,the improved LSTMand SA-ConvLSTMare employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images,respectively.Subsequently,a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities,enhancing the capacity to focus on the most relevant features.Finally,theTransformermodel is applied to further capture the short-termtemporal patterns and long-term dependencies within multi-modal feature information.The paper also compares the proposed method with various competitive methods.The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.展开更多
Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variat...Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition(VMD)and Channel Attention Mechanism.First,Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power.Second,the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition(VMD).Finally,the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model.In this model,the convolutional neural network(CNN)and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features.The extracted data is then fed into the bidirectional long short-term memory network(BiLSTM)to capture the time-series features,and the final output is the prediction result.The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China.The results show that compared with other prediction methods,the method proposed in this paper has a higher prediction accuracy,which helps to improve the proportion of distributed PV access to the grid,and can guarantee the safe and stable operation of the power grid.展开更多
The penetration rate of new wind and photovoltaic energy in the power system has increased significantly,and the dramatic fluctuation of the net load of the grid has led to a severe lack of flexibility in the regional...The penetration rate of new wind and photovoltaic energy in the power system has increased significantly,and the dramatic fluctuation of the net load of the grid has led to a severe lack of flexibility in the regional grid.This paper proposes a hierarchical optimal dispatch strategy for a high proportion of new energy power systems that considers the balanced response of grid flexibility.Firstly,various flexibility resource regulation capabilities on the source-load side are analyzed,and then flexibility demand and flexibility response are matched,and flexibility demand response assessment is proposed;then,a hierarchical optimal dispatch model of the grid taking flexibility adjustment capability into account is established,and the upper model optimizes the net load curve with the objectives of minimizing the fluctuation of the net load,maximizing the benefits of energy storage and controllable loads,and optimizing the flexibility adjustment capability.The upper layer model optimizes the net load curve by minimizing net load fluctuation,maximizing energy storage and controllable load revenue,and optimizing flexibility adjustment capability.In contrast,the lower layer model optimizes the power allocation of thermal power units and regulates the lost load of wind and solar power generation by minimizing the total system operating cost.The results show that the proposed strategy improves the flexibility of the grid by 15.2%,gives full play to the regulation capability of each flexibility resource,and reduces the fluctuation of the net load by 15.6%to achieve optimal coordination between different types of flexibility resources.展开更多
为充分挖掘源-荷侧可调度资源,优化综合能源系统(integrated energy system,IES)的经济性能和低碳效益,提出一种考虑源-荷协调响应的综合能源系统优化调度策略。在能源供给侧,利用余热设备和电-氢耦合系统,构建电、热能灵活转换与输出...为充分挖掘源-荷侧可调度资源,优化综合能源系统(integrated energy system,IES)的经济性能和低碳效益,提出一种考虑源-荷协调响应的综合能源系统优化调度策略。在能源供给侧,利用余热设备和电-氢耦合系统,构建电、热能灵活转换与输出的源侧响应模型;在负荷需求侧,综合考虑多元负荷的中断、时移等响应特性,并结合电动汽车充电站的运行约束,构建负荷侧响应模型;为评估综合能源系统负荷需求的综合满意程度,提出了一种负荷满意度综合评价指标,通过商业求解器对综合能源系统优化模型进行仿真求解。仿真结果表明,所提策略在保障用能质量的同时有效降低了系统运行成本。展开更多
深度挖掘柔性资源的响应潜力,可以提升园区综合能源系统(Park Integrated Energy System,PIES)运行的经济性、灵活性与低碳性。为此,文章提出一种考虑供需灵活响应的PIES优化调度策略。首先,在分析设备动态能效特性的基础上,引入有机闪...深度挖掘柔性资源的响应潜力,可以提升园区综合能源系统(Park Integrated Energy System,PIES)运行的经济性、灵活性与低碳性。为此,文章提出一种考虑供需灵活响应的PIES优化调度策略。首先,在分析设备动态能效特性的基础上,引入有机闪蒸循环和电热锅炉建立热电联产(Combined Heat and Power,CHP)机组多模态且变工况运行的供给侧灵活响应模型;然后,基于多级阶梯激励补偿机制,在负荷侧建立削减、替代与转移柔性负荷差异化配比的电-冷-热多元需求响应模型;最后,以日运行成本最小为优化目标,构建PIES供需灵活响应的协调优化模型。仿真分析表明,文章所提策略不仅可以提高调度模型的准确度,还可以在促进风电消纳的同时,实现系统经济性、低碳性,供能可靠性、满意度的多维度优化平衡。展开更多
基金supported by the Science and Technology Project of Jiangsu Coastal Power Infrastructure Intelligent Engineering Research Center“Photovoltaic Power Prediction System Driven by Deep Learning and Multi-Source Data Fusion”(F2024-5044).
文摘Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challenges for its extensive incorporation into power grids.Thus,enhancing the precision of PV power prediction is particularly important.Although existing studies have made progress in short-term prediction,issues persist,particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data.These factors hinder improvements in PV power prediction performance.To overcome these challenges,this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning.First,the improved LSTMand SA-ConvLSTMare employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images,respectively.Subsequently,a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities,enhancing the capacity to focus on the most relevant features.Finally,theTransformermodel is applied to further capture the short-termtemporal patterns and long-term dependencies within multi-modal feature information.The paper also compares the proposed method with various competitive methods.The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.
基金supported by the Inner Mongolia Power Company 2024 Staff Innovation Studio Innovation Project“Research on Cluster Output Prediction and Group Control Technology for County-Wide Distributed Photovoltaic Construction”.
文摘Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition(VMD)and Channel Attention Mechanism.First,Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power.Second,the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition(VMD).Finally,the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model.In this model,the convolutional neural network(CNN)and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features.The extracted data is then fed into the bidirectional long short-term memory network(BiLSTM)to capture the time-series features,and the final output is the prediction result.The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China.The results show that compared with other prediction methods,the method proposed in this paper has a higher prediction accuracy,which helps to improve the proportion of distributed PV access to the grid,and can guarantee the safe and stable operation of the power grid.
文摘The penetration rate of new wind and photovoltaic energy in the power system has increased significantly,and the dramatic fluctuation of the net load of the grid has led to a severe lack of flexibility in the regional grid.This paper proposes a hierarchical optimal dispatch strategy for a high proportion of new energy power systems that considers the balanced response of grid flexibility.Firstly,various flexibility resource regulation capabilities on the source-load side are analyzed,and then flexibility demand and flexibility response are matched,and flexibility demand response assessment is proposed;then,a hierarchical optimal dispatch model of the grid taking flexibility adjustment capability into account is established,and the upper model optimizes the net load curve with the objectives of minimizing the fluctuation of the net load,maximizing the benefits of energy storage and controllable loads,and optimizing the flexibility adjustment capability.The upper layer model optimizes the net load curve by minimizing net load fluctuation,maximizing energy storage and controllable load revenue,and optimizing flexibility adjustment capability.In contrast,the lower layer model optimizes the power allocation of thermal power units and regulates the lost load of wind and solar power generation by minimizing the total system operating cost.The results show that the proposed strategy improves the flexibility of the grid by 15.2%,gives full play to the regulation capability of each flexibility resource,and reduces the fluctuation of the net load by 15.6%to achieve optimal coordination between different types of flexibility resources.
文摘为充分挖掘源-荷侧可调度资源,优化综合能源系统(integrated energy system,IES)的经济性能和低碳效益,提出一种考虑源-荷协调响应的综合能源系统优化调度策略。在能源供给侧,利用余热设备和电-氢耦合系统,构建电、热能灵活转换与输出的源侧响应模型;在负荷需求侧,综合考虑多元负荷的中断、时移等响应特性,并结合电动汽车充电站的运行约束,构建负荷侧响应模型;为评估综合能源系统负荷需求的综合满意程度,提出了一种负荷满意度综合评价指标,通过商业求解器对综合能源系统优化模型进行仿真求解。仿真结果表明,所提策略在保障用能质量的同时有效降低了系统运行成本。
文摘虚拟同步机(virtual synchronous generator,VSG)控制缓解了新型电力系统低惯量弱阻尼特性,但也引入了功角振荡,导致功角稳定性问题。已有研究从控制参数自适应以及控制环重构角度改进VSG控制,但存在设计困难、物理意义不明确等问题。因此,该文首先基于等面积定则(equal area criterion,EAC),利用虚拟阻抗,提出“功角能量”快速衰减的改进控制思路,从图形解法解释了底层物理意义,并设计算法求解控制各阶段虚拟阻抗大小。在此基础上,分析改进控制思路可能存在的问题。其次,从补偿“阻尼功”缺失和提高控制适用性角度,构建基于虚拟阻抗频率自适应的控制策略,以实现不同工况下功角振荡的优化抑制。最后,通过电磁暂态仿真验证前述分析的正确性,并展示所提控制策略对功角振荡抑制以及功角稳定性提高的有效性。
文摘深度挖掘柔性资源的响应潜力,可以提升园区综合能源系统(Park Integrated Energy System,PIES)运行的经济性、灵活性与低碳性。为此,文章提出一种考虑供需灵活响应的PIES优化调度策略。首先,在分析设备动态能效特性的基础上,引入有机闪蒸循环和电热锅炉建立热电联产(Combined Heat and Power,CHP)机组多模态且变工况运行的供给侧灵活响应模型;然后,基于多级阶梯激励补偿机制,在负荷侧建立削减、替代与转移柔性负荷差异化配比的电-冷-热多元需求响应模型;最后,以日运行成本最小为优化目标,构建PIES供需灵活响应的协调优化模型。仿真分析表明,文章所提策略不仅可以提高调度模型的准确度,还可以在促进风电消纳的同时,实现系统经济性、低碳性,供能可靠性、满意度的多维度优化平衡。
